Diagnosis & Failure Localisation

Chapter 13 · Section 13.4 · DFT Debug Methodology

Project thread — the mini-SoC's real defect localized: cone-intersection to a ranked candidate, a diagnostic mode to beat compression, PFA to confirm, and volume diagnosis for yield.

1. Why Should I Learn This?

Diagnosis turns a confirmed defect into a place to look — a ranked candidate list precise enough to point PFA and, in volume, to drive yield learning.

• An inverse problem: from failing patterns/cells back to the likely fault site by intersecting back-cones (consistent with passing cells).
• Output: a ranked candidate list, a suspected fault type (stuck-at/bridge/open/transition), an xy/layer hint — diagnosis localizes, PFA proves.
• Resolution ~ observability: many observers → tight; compression hides the failing cell → looser → use a diagnostic mode (7.x tradeoff).
• Volume diagnosis → a Pareto of sites → systematic vs random → yield engineering.

2. Real Silicon Story — the diagnosis compression blurred

A part had a confirmed real defect (it survived the 13.3 triage), but diagnosis returned a loose candidate list — dozens of suspects spread across the die. PFA can't be pointed at dozens of sites; deprocessing is slow and destructive. The debug stalled.

The cause was compression. The design used an on-chip compactor (7.x): many scan cells fed a compactor that produced a compressed signature, so the datalog recorded which signature bit miscompared, not which exact cell — hiding the observability diagnosis needs to intersect cones tightly. The fix was a DIAGNOSTIC mode: re-run the failing patterns with the compactor bypassed (a high-resolution / bypass mode) so each individual cell's pass/fail was visible again. With full observability restored, diagnosis intersected the failing cones to a few candidates, and PFA confirmed a bridge at the top suspect. Lesson: diagnosis resolution depends on observability, and compression trades diagnosability for test cost (7.x) — when diagnosis is loose, un-compact with a diagnostic mode. And remember the boundary: diagnosis localized the bridge to a candidate; PFA proved it — the two are different jobs.

3. Factory Perspective — diagnosis through each lens

• What the test/DFT engineer sees: the diagnosis run — feed failing patterns/cells in, get a ranked candidate list + fault type; switch to a diagnostic mode if compression blurs it.
• What the failure-analysis engineer sees: the candidate + xy/layer hint as a PFA target — where to deprocess/image to confirm the physical defect.
• What the yield/process engineer sees: the volume Pareto — is the same site failing across many dies (systematic → process/design) or scattered (random)?
• What management cares about: that diagnosis shortens PFA (cost/time) and drives yield learning — turning test failures into process improvements, not just scrapped dies.

4. Concept — the inverse problem, cones, resolution, volume

What diagnosis is:

• An inverse problem: given failing patterns + their failing cells (datalog) + the netlist + the ATPG fault model, compute the fault candidates (nets/pins) that best explain the observed failures.
• Direction: from observed failures → back to the likely fault site.

How it works — cone intersection:

• Each failing compare cell has a logic cone feeding it; the defect must lie in the intersection of the cones of the failing cells.
• It must be consistent with the passing cells — a candidate that would make a passing cell fail is rejected.
• More failing patterns that observe a site → a tighter candidate set.

Output:

• A ranked candidate list (top suspects + confidence), a suspected fault type (stuck-at / bridge / open / transition), often a physical (x,y) / layer hint.
• Boundary: diagnosis localizes; it does not prove. PFA (deprocess/image) confirms the physical defect.

Resolution ~ observability:

• Many patterns/cells observe the site → tight (few candidates). Poor observability → loose (many candidates).
• Compression (7.x) reduces resolution — the compactor hides which exact cell failed → may need a DIAGNOSTIC (bypass / hi-res) mode to un-compact. A real coverage-vs-diagnosability tradeoff.

Fault-model match & signature:

• Chain diagnosis (13.2) for chain defects; logic (scan) diagnosis for logic defects.
• The failure signature (which patterns fail, stuck-at vs transition, systematic vs random) suggests the defect class (bridge / open / stuck).

Volume diagnosis → yield learning:

• Aggregate diagnosis across many failing dies → a Pareto of failing sites/layers.
• Systematic (same site, many dies) = a design/process marginality (fix the mechanism); random = particle defects. This is how DFT drives yield learning.

Tool boundary (garbage in, garbage out):

• Diagnosis gives candidates + confidence, not certainty; PFA confirms.
• A wrong fault model or unmasked X degrades diagnosis → clean the pattern debug (13.3) first.

5. Mental Model — triangulating a signal from multiple towers

Diagnosis is like triangulating a hidden transmitter from multiple receiving towers — the more towers that hear it, the tighter you pin its location.

• Each failing cell is a tower that heard the signal (the defect's effect); its cone is the direction it heard from. The transmitter (the defect) must be where the directions cross — the intersection of the failing cones.
• The towers that didn't hear it (the passing cells) are just as useful: they rule out whole regions (if the defect were there, they'd have heard it) — so they constrain the fix.
• More towers hearing it → a tighter fix (fewer candidates). Few towers (poor observability) → a fuzzy fix (many candidates). If a relay tower (a compactor) merges several towers' reports into one summary, you lose bearing — so you switch to direct reception (a diagnostic/bypass mode) to get each tower's report individually.
• Triangulation gives you a likely spot (candidates + confidence) — but you still send a crew to dig (PFA) to prove the transmitter is there. And if every failing chip triangulates to the same spot, that's not bad luck — it's a broken tower on the network (a systematic process/design issue) to fix at the source (volume diagnosis → yield).

Triangulate the defect from the towers that heard it (failing cells' cones), ruled in by crossings and out by the silent towers (passing cells) — more towers = tighter; a relay (compactor) blurs bearing; a crew (PFA) proves it; same-spot-everywhere = a systematic fix.

6. Working Example — from failing datalog to a ranked candidate

Trace a diagnosis from failing cells to a candidate for PFA:

# Diagnosis (logic/scan) - REPRESENTATIVE, tool-neutral:
  INPUT : failing patterns + failing cells (datalog, from a CLEAN pattern debug 13.3) + netlist + fault model
  STEP 1 - CONE INTERSECTION:
     failing cell A <- cone {n1,n2,n5,n8}
     failing cell B <- cone {n5,n8,n9}
     intersection (explains BOTH fails) = {n5, n8}
  STEP 2 - CONSISTENCY WITH PASSES:
     passing cell C <- cone {n8, n12}  ; if the defect were on n8, C would ALSO fail -> C PASSED -> REJECT n8
     -> surviving candidate = {n5}
  STEP 3 - RANK + FAULT TYPE:
     candidate n5, confidence high ; signature (fails only transition patterns, not stuck-at) -> suspect a slow/OPEN or bridge
     physical hint: n5 at (x,y), metal-2
  OUTPUT: ranked candidate list [n5 (0.9), ...], fault type = transition/open, xy/layer for PFA
  --- RESOLUTION caveat ---
     if COMPRESSION (7.x) is on, the datalog shows a compacted SIGNATURE bit, not cells A/B/C individually
     -> LOOSE candidate list -> re-run failing patterns in a DIAGNOSTIC (bypass/hi-res) mode -> restore observability -> tighten
  --- BOUNDARY ---   diagnosis LOCALIZES (n5, candidate) ; PFA (deprocess/image at x,y) PROVES the physical defect
  --- VOLUME ---     aggregate n5-type hits across many dies -> if SYSTEMATIC (same site) -> design/process fix (yield), not just this die

Resolution is a ladder set by observability — compression lowers a rung, a diagnostic mode restores it:

7. Industry Flow — diagnose, confirm, and (in volume) learn

Diagnosis localizes to candidates, PFA confirms, and volume diagnosis turns failures into yield learning:

8. Debugging Session — a real defect that diagnosed loose

9. Common Mistakes

• Diagnosing on a dirty datalog. Unmasked X / bad models (13.3) → garbage-in, garbage-out — clean the pattern debug first.
• Expecting tight resolution under compression. The compactor hides the failing cell (7.x) → use a diagnostic/bypass mode.
• Treating candidates as proof. Diagnosis localizes; PFA proves — candidates have confidence, not certainty.
• Ignoring the passing cells. Passes constrain the candidate set (reject candidates that would fail a pass).
• Missing the systematic signal. Same site across many dies = a process/design fix (volume diagnosis), not just a scrapped die.

10. Industry Best Practices

• Feed diagnosis a clean datalog (X masked, models right, 13.3) — resolution is only as good as the input.
• Use the failing and passing cells — intersection + consistency give the ranked candidates.
• Switch to a diagnostic mode when compression makes the result loose (7.x) — restore observability.
• Treat candidates as PFA targets, not verdicts — PFA confirms the physical defect.
• Run volume diagnosis — aggregate a Pareto to separate systematic (fixable mechanism) from random.

11. Senior Engineer Thinking

• Beginner: "Diagnosis gave dozens of candidates — the tool's bad, or there are many defects."
• Senior: "A loose list on a compressed design means missing observability, not a bad tool — the compactor hid which cell failed (7.x). I re-run in a diagnostic/bypass mode to un-compact, and the candidates collapse to a few. Then PFA proves the top one — diagnosis localizes, PFA proves. And I check volume: if this site fails across many dies, it's systematic — a process/design fix, not just a scrap. Garbage in, garbage out — so I only diagnose a clean datalog."

The senior reads loose resolution as an observability problem, uses a diagnostic mode, and separates localization (diagnosis) from proof (PFA).

12. Silicon Impact

Diagnosis is the step that converts a confirmed defect into an actionable location — and, in aggregate, converts test failures into yield learning. It is fundamentally an inverse problem: from the observed failures (which patterns, which cells) plus the netlist and fault model, compute the fault candidates that best explain what was seen — reasoning backward from effect to likely cause. The engine is back-cone intersection: the defect must lie in the intersection of the failing cells' cones and be consistent with the passing cells (a candidate that would have failed a passing cell is rejected), so more failing observations tighten the set. The output — a ranked candidate list, a suspected fault type (stuck-at/bridge/open/transition), and an xy/layer hint — exists to point PFA, which is the essential boundary: diagnosis localizes; PFA proves. Because the method is observation-driven, its resolution tracks observability: many observers → tight, few → loose — and this collides productively with compression (7.x), which hides which exact cell failed behind a compacted signature, lowering resolution. That's a real coverage-vs-diagnosability tradeoff, resolved by a DIAGNOSTIC (bypass/hi-res) mode that un-compacts and restores per-cell observability — the reason diagnosable, compressed designs provide such a mode. Two boundaries keep diagnosis honest: garbage in, garbage out (a wrong fault model or unmasked X from a dirty 13.3 debug degrades the result — so clean the pattern debug first), and candidates are confidence, not certainty (PFA is the arbiter). The highest-leverage use is volume diagnosis: aggregating diagnosis across many failing dies yields a Pareto of failing sites/layers that distinguishes systematic failures (the same site across many dies → a design/process marginality to fix at the mechanism) from random particle defects — turning DFT from per-die pass/fail into a yield-learning engine that feeds process and design. For the failure-analysis engineer, diagnosis is a PFA target; for the yield/process engineer, it's a Pareto that directs improvement; for the DFT engineer, it's the payoff of clean chain (13.2) and pattern (13.3) debug; and for the program, it's how a debugged failure becomes fewer failures next lot. The next lesson (13.5) takes this whole localize-and-confirm discipline onto the tester at silicon bring-up, where the environment is live and the pressure is highest.

13. Engineering Checklist

• Fed diagnosis a clean datalog (X masked, models right, 13.3) — no garbage-in.
• Used failing + passing cells — cone intersection + consistency → ranked candidates.
• Switched to a diagnostic/bypass mode when compression made the result loose (7.x).
• Treated candidates as PFA targets (with confidence) — PFA confirms the physical defect.
• Ran volume diagnosis — a Pareto to separate systematic (mechanism fix) from random.

14. Try Yourself

1. Explain diagnosis as an inverse problem — from failing cells back to a candidate site via cone intersection.
2. Show how the passing cells constrain (reject) candidates — and why more failing observers → tighter.
3. State the boundary: diagnosis localizes, PFA proves — and what each produces.
4. Explain why compression lowers diagnostic resolution and how a diagnostic/bypass mode restores it (7.x).
5. Describe volume diagnosis → a Pareto → systematic vs random → yield learning.

The diagnosis reasoning is tool-neutral; diagnosis is a DFT tool, PFA is a lab process, volume Pareto is yield engineering. No paid tool required to reason about localization.

15. Interview Perspective

• Weak: "Diagnosis tells you where the defect is."
• Good: "Diagnosis intersects the failing cells' cones to give candidate sites, and PFA confirms the physical defect."
• Senior: "Diagnosis is an inverse problem: from the failing patterns/cells (a clean datalog — garbage in, garbage out) plus the netlist and fault model, it computes candidates by intersecting the failing cells' back-cones, consistent with the passing cells, and ranks them with a fault type and xy/layer hint. Its resolution tracks observability — more observers, tighter — so compression (7.x) lowers it by hiding which cell failed, and I un-compact with a diagnostic/bypass mode when it's loose. The boundary is firm: diagnosis localizes, PFA proves. And the big win is volume diagnosis — aggregate across many dies into a Pareto to separate systematic (a process/design fix) from random, so DFT drives yield learning, not just per-die scrap."

16. Interview / Review Questions

17. Key Takeaways

• Diagnosis is an inverse problem: from failing patterns/cells + netlist + fault model back to the fault candidates that best explain the observed failures — by intersecting the failing cells' back-cones, consistent with the passing cells (more failing observers → tighter).
• Output: a ranked candidate list (+ confidence), a suspected fault type (stuck-at/bridge/open/transition), and an xy/layer hint — but diagnosis localizes, it does not prove; PFA (deprocess/image) confirms.
• Resolution ~ observability: compression (7.x) hides which cell failed → loose candidates → use a DIAGNOSTIC (bypass/hi-res) mode to un-compact (a real coverage-vs-diagnosability tradeoff).
• Volume diagnosis aggregates across many dies into a Pareto of sites/layers → systematic (same site → design/process fix) vs random → yield learning (DFT beyond per-die pass/fail).
• Garbage in, garbage out: a wrong fault model or unmasked X degrades diagnosis → clean the pattern debug (13.3) first; diagnosis gives candidates + confidence, not certainty. Next: 13.5 — silicon bring-up & tester debug.

18. Quick Revision

Diagnosis & failure localisation. A mismatch that survived the fork (13.3) is REAL → DIAGNOSIS = an INVERSE problem: from failing patterns/cells + netlist + fault model → fault CANDIDATES by INTERSECTING the failing cells' back-cones, consistent with the PASSING cells (more failing observers = tighter). Output: ranked candidates + confidence + fault type (stuck-at/bridge/open/transition) + xy/layer. Boundary: diagnosis LOCALIZES, PFA PROVES (deprocess/image). Resolution ~ observability → compression (7.x) hides the failing cell → LOOSE → re-run in a DIAGNOSTIC (bypass/hi-res) mode to un-compact. VOLUME diagnosis → aggregate many dies → a Pareto → systematic (same site → design/process fix) vs random → yield learning. Garbage in = garbage out → clean 13.3 first. Next: 13.5 — silicon bring-up & tester debug.